Infusion Stand Tipping and Abnormal State Detection Image Dataset

In the medical industry, the tipping and abnormal states of infusion stands are important safety hazards, especially in emergency and intensive care scenarios. Current monitoring methods primarily rely on manual inspections, which are inefficient and prone to omissions. Existing automatic monitoring systems still have deficiencies in image recognition accuracy and real-time processing. This dataset aims to improve the accuracy and efficiency of abnormal state detection by providing a large amount of high-quality infusion stand images to support the training of deep learning models. The dataset contains images of infusion stands from different hospital environments, using advanced imaging equipment to ensure image clarity and diversity. Quality control measures for the data include multiple rounds of annotation and expert review to ensure consistency and accuracy of the annotations. Data storage is in JPEG format, organized in a folder structure, facilitating subsequent processing and access. The core advantage of this dataset is its high annotation accuracy and consistency, with an annotation error rate of less than 2%. New data augmentation techniques such as random cropping and rotation have been adopted, significantly enhancing the robustness of the model. Models trained using this dataset have improved object detection accuracy by 15%, effectively reducing the occurrence of medical accidents.